library(DESeq2)
library(ggplot2)
library(reshape2)
library(patchwork)
library(ggrepel)
library(cowplot)
library(grid)
library(RColorBrewer)
library(repr) 
library(glmpca)
library(pheatmap)
library(PoiClaClu)
library(apeglm)
library(ashr)
library(vsn)
library(dplyr)
library(tidyr)
library(viridis)  
library("pheatmap")
library("ReportingTools")
library("BiocParallel")
library(glmpca)
library(emdbook)  
library(tidyverse)
register(MulticoreParam(4))
library(sva)
library(RUVSeq)
library(GenomicRanges)
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)
library(data.table)
library(biomaRt)
library(ensembldb)
library(AnnotationHub)
library(data.table)

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Welcome to Bioconductor

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ChIPseeker v1.42.1 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Qianwen Wang, Ming Li, Tianzhi Wu, Li Zhan, Lin Li, Meijun Chen, Wenqin
Xie, Zijing Xie, Erqiang Hu, Shuangbin Xu, Guangchuang Yu. Exploring
epigenomic datasets by ChIPseeker. Current Protocols. 2022, 2(10): e585

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# Load count data
count_data <- read.csv("A2780_peak_counts.txt", 
                       skip = 1, sep="\t", header=TRUE, stringsAsFactors=FALSE)  

head(count_data,2 )

# Remove the ".genomicAllAligned.sorted.bam" suffix from column names
colnames(count_data) <- sub("\\.CLAM.genomicAllAligned\\.sorted\\.bam$", "", colnames(count_data))
head(count_data, 2)
colnames(count_data)

colnames(count_data) <- sub("^([^\\.]+)\\..*", "\\1", colnames(count_data))
head(count_data, 2)
colnames(count_data, 2)

count_data2 <- count_data %>%
  mutate(
    Chr = sapply(strsplit(Chr, ";"), `[`, 1),
    Start = sapply(strsplit(Start, ";"), function(x) min(as.integer(x))),
    End = sapply(strsplit(End, ";"), function(x) max(as.integer(x)))
  )

colnames(count_data2)
head(count_data2, 2)

colnames(count_data2)
head(count_data2, 8)

print("number of peaks:")
dim(count_data2)[1]

# Convert to GRanges object
gr <- GRanges(
  seqnames = count_data2$Chr,
  ranges = IRanges(start = count_data2$Start, end = count_data2$End)
)

# Merge overlCS13ping intervals
gr_merge <- GenomicRanges::reduce(gr)

# Convert bDC13k to data frame
df_merge <- as.data.frame(gr_merge)[, c("seqnames", "start", "end")]
colnames(df_merge) <- c("Chr", "Start", "End")

# Print or return merged peaks
dim(df_merge)[1]

# Annotate merged peaks to genome features
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

peak_anno <- annotatePeak(gr_merge, 
                          TxDb = txdb, 
                          tssRegion = c(-3000, 3000), 
                          annoDb = "org.Hs.eg.db")

# 📊 Plot annotation breakdown 
print("the annotation of the peaks:")
plotAnnoPie(peak_anno)

[1] "number of peaks:"

>> preparing features information...		 2025-04-21 05:41:50 AM 
>> identifying nearest features...		 2025-04-21 05:41:51 AM 
>> calculating distance from peak to TSS...	 2025-04-21 05:41:52 AM 
>> assigning genomic annotation...		 2025-04-21 05:41:52 AM 
>> adding gene annotation...			 2025-04-21 05:42:12 AM

'select()' returned 1:many mapping between keys and columns

>> assigning chromosome lengths			 2025-04-21 05:42:12 AM 
>> done...					 2025-04-21 05:42:12 AM 
[1] "the annotation of the peaks:"

sample_cols = colnames(count_data2)[7:ncol(count_data2)]
head(sample_cols)
ncol(count_data2)

# Identify sample columns only
sample_cols <- colnames(count_data2)[7:ncol(count_data2)]  # assuming first 3 are metadata

# Now separate based on names ending in "in"
in_cols    <- grep("in$", sample_cols, value = TRUE)
no_in_cols <- grep("in$", sample_cols, value = TRUE, invert = TRUE)

sample_cols
in_cols
no_in_cols

head(count_data2, 2)

print("separate the large dataframe into two sub dataframes, that contain IP samples and non-IP(input) samples")

[1] "separate the large dataframe into two sub dataframes, that contain IP samples and non-IP(input) samples"

# Create data frames with Geneid + in/out columns

# ip <- count_data2 %>% 
#  select(Geneid, all_of(no_in_cols))

# np <- count_data2 %>% 
#  select(Geneid, all_of(in_cols))

# head(ip, 2)
# head(np, 2)

# Create data frames with Geneid + in/out columns
ip <- count_data2[, c("Geneid", no_in_cols)]
np  <- count_data2[, c("Geneid", in_cols)]

# Quick check
head(ip, 2)
head(np, 2)

# Select _IP columns + Geneid
# ip <- count_data2 %>% dplyr::select(Geneid, ends_with("_IP"))
# head(ip, 2)

# Select _NP columns + Geneid
# np <- count_data2 %>% dplyr::select(Geneid, ends_with("_NP"))
# head(np, 2)

print("DESeq2 to measure the differential binding across IP samples :")

[1] "DESeq2 to measure the differential binding across IP samples :"

# Step 1: Select only columns ending with "_IP" and include Geneid for rownames
# ip <- count_data2 %>%
#      dplyr::select(Geneid, ends_with("_IP"))

# Step 2: Set Geneid as rownames and remove Geneid column
rownames(ip) <- ip$Geneid
ip <- ip[, -1]

# Step 3: Verify column names
sample_names <- colnames(ip)
print(sample_names)

# Step 4: Assign condition groups (DC13, CS13, pla13)
conditions <- ifelse(grepl("^CS13", sample_names), "CS13",
              ifelse(grepl("^DC13", sample_names), "DC13",
              ifelse(grepl("^pla13", sample_names), "pla13", NA)))
print(conditions)

# Step 5: Error handling if any unrecognized sample names
if (any(is.na(conditions))) {
  stop("Some sample names do not match expected patterns (CS13, DC13, pla13). Check column names!")
}

# Step 6: Create colData
col_data <- data.frame(
  row.names = sample_names,
  condition = factor(conditions, levels = c("CS13", "DC13", "pla13"))
)
print("col data:")
print(col_data)

# Step 7: Remove rows with NA values
dim(ip)
ip <- ip[complete.cases(ip), ]
dim(ip)

# Step 8: Compute summary statistics directly on ip (only numeric columns)
summary_stats <- data.frame(
  Median = apply(ip, 2, median, na.rm = TRUE),
  Min    = apply(ip, 2, min, na.rm = TRUE),
  Max    = apply(ip, 2, max, na.rm = TRUE)
)

# Step 9: Print the result
print(summary_stats)

[1] "CS13_1"  "CS13_2"  "DC13_1"  "DC13_2"  "pla13_1" "pla13_2"
[1] "CS13"  "CS13"  "DC13"  "DC13"  "pla13" "pla13"
[1] "col data:"
        condition
CS13_1       CS13
CS13_2       CS13
DC13_1       DC13
DC13_2       DC13
pla13_1     pla13
pla13_2     pla13

        Median Min    Max
CS13_1     150   0 442108
CS13_2     147   0 390848
DC13_1     157   0 560663
DC13_2     181   0 445104
pla13_1    147   0 511193
pla13_2    288   0 720386

# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(countData = ip, colData = col_data, design = ~condition)

# Set reference level for condition (pla13 will be the baseline)
dds$condition <- relevel(dds$condition, ref = "pla13")

# Print number of genes before filtering
cat("Number of genes before filtering:", nrow(dds), "\n")

# Estimate size fDC13tors (required for normalization)
dds <- estimateSizeFactors(dds)

# Filter: keep genes with normalized count >= 4 in at least 4 samples
keep <- rowSums(counts(dds, normalized = TRUE) >= 4) >= 4
dds <- dds[keep, ]

# Print number of genes after filtering
cat("Number of genes after filtering:", nrow(dds), "\n")

# Run DESeq2 differential expression analysis
dds <- DESeq(dds)

# ExtrDC13t results table
# res <- results(dds) 
# it will produce : Wald test p-value: condition DC13 vs pla13 

# View summary of results
# cat("First row of DE results:\n")
# print(head(res, 1))
# cat("Last row of DE results:\n")
# print(tail(res, 1))
# cat("Summary of DESeq2 results:\n")
# print(summary(res))

# Show contrast names
cat("available result contrasts:\n")
print(resultsNames(dds))

# Size fDC13tors (re-estimation here is harmless but redundant)
cat("The size factors are:\n")
print(sizeFactors(dds))

# ExtrDC13t normalized counts
norm_counts <- counts(dds, normalized = TRUE)

# Preview normalized counts
cat("Preview of normalized counts:\n")
print(head(norm_counts, 2))

# Spla13e normalized counts to CSV
write.csv(norm_counts, "A2780.peaks.IP.samples.normalized.counts.csv", row.names = TRUE)

# Compute summary statistics for eDC13h sample (column)
summary_stats2 <- data.frame(
  Median = apply(norm_counts, 2, median, na.rm = TRUE),
  Min    = apply(norm_counts, 2, min, na.rm = TRUE),
  Max    = apply(norm_counts, 2, max, na.rm = TRUE)
)

# Print summary statistics
cat("Summary of the normalized counts:\n")
print(round(summary_stats2, 2))

# Show pla13ailable assays in the DESeqDataSet
cat("Available assays in dds:\n")
print(names(assays(dds)))

# View fitted means (mu) and Cook's distances
cat("DESeq2 fitted means (mu):\n")
print(head(assay(dds, "mu"), 2))

cat("DESeq2 Cook's distances (cooks):\n")
print(head(assay(dds, "cooks"), 2))

Number of genes before filtering: 8855 
Number of genes after filtering: 7860

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

available result contrasts:
[1] "Intercept"               "condition_CS13_vs_pla13"
[3] "condition_DC13_vs_pla13"
The size factors are:
   CS13_1    CS13_2    DC13_1    DC13_2   pla13_1   pla13_2 
0.8789895 0.8547268 0.9296571 1.0728089 0.8404988 1.6848888 
Preview of normalized counts:
                   CS13_1    CS13_2    DC13_1    DC13_2   pla13_1   pla13_2
ENSG00000290825  17.06505  11.69965  19.36198  25.16758  24.98516  46.29386
ENSG00000310526 152.44778 129.86606 207.60343 127.70214 196.31200 196.45213
Summary of the normalized counts:
        Median  Min      Max
CS13_1  222.98 0.00 502973.0
CS13_2  221.12 0.00 457278.3
DC13_1  221.59 0.00 603085.8
DC13_2  219.05 0.00 414895.9
pla13_1 223.68 0.00 608201.9
pla13_2 213.07 0.59 427557.0
Available assays in dds:
[1] "counts" "mu"     "H"      "cooks" 
DESeq2 fitted means (mu):
                   CS13_1    CS13_2    DC13_1    DC13_2   pla13_1   pla13_2
ENSG00000290825  12.65335  12.30408  20.74278  23.93683  30.37423  60.88909
ENSG00000310526 124.09920 120.67369 155.51037 179.45641 165.06087 330.88592
DESeq2 Cook's distances (cooks):
                    CS13_1     CS13_2     DC13_1     DC13_2      pla13_1
ENSG00000290825 0.06652731 0.06546654 0.03480565 0.03670431 1.819260e-01
ENSG00000310526 0.07147939 0.07086709 0.64761528 0.66979073 1.415852e-06
                     pla13_2
ENSG00000290825 2.112829e-01
ENSG00000310526 1.605512e-06

# Extract raw (unnormalized) counts
print("raw counts")
raw_counts <- counts(dds, normalized = FALSE)
head(raw_counts, 2)

# Extract normalized counts
print("norm counts")
norm_counts <- counts(dds, normalized = TRUE)
head(norm_counts, 2)

[1] "raw counts"

[1] "norm counts"

print("Boxplot of Raw vs log2 Normalized Counts")

# Prepare raw counts
raw_counts <- as.data.frame(counts(dds, normalized = FALSE))
raw_counts$Gene <- rownames(raw_counts)
raw_long <- pivot_longer(raw_counts, -Gene, names_to = "Sample", values_to = "Count")
raw_long$log2_count <- log2(raw_long$Count + 1)

# Prepare normalized counts
norm_counts <- as.data.frame(counts(dds, normalized = TRUE))
norm_counts$Gene <- rownames(norm_counts)
norm_long <- pivot_longer(norm_counts, -Gene, names_to = "Sample", values_to = "Count")
norm_long$log2_count <- log2(norm_long$Count + 1)

# Color palette
sample_list <- unique(c(raw_long$Sample, norm_long$Sample))
sample_colors <- setNames(viridis::viridis(length(sample_list), option = "D"), sample_list)

# Plot p1: Raw counts
p1 <- ggplot(raw_long, aes(x = Sample, y = log2_count, fill = Sample)) +
  geom_boxplot(outlier.size = 0.5, width = 0.7) +
  scale_fill_manual(values = sample_colors, name = "Sample") +
  theme_minimal(base_size = 12) +
  labs(title = "Raw Counts (log2 scale)",
       y = "log2(Counts + 1)", x = "Sample") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Plot p2: Normalized counts
p2 <- ggplot(norm_long, aes(x = Sample, y = log2_count, fill = Sample)) +
  geom_boxplot(outlier.size = 0.5, width = 0.7) +
  scale_fill_manual(values = sample_colors, name = "Sample") +
  theme_minimal(base_size = 12) +
  labs(title = "Normalized Counts (log2 scale)",
       y = "log2(Counts + 1)", x = "Sample") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')

[1] "Boxplot of Raw vs log2 Normalized Counts"

# Extract raw counts and log-transform
raw_counts <- counts(dds, normalized = FALSE)
raw_log_counts <- log10(raw_counts + 1)

# Convert to long format for ggplot2
log1_df <- as.data.frame(raw_log_counts)
log1_df$Gene <- rownames(log1_df)
log1_long <- pivot_longer(log1_df, -Gene, names_to = "Sample", values_to = "log10_count")

# Extrac13t normalized counts and log-transform
norm_counts <- counts(dds, normalized = TRUE)
norm_log_counts <- log10(norm_counts + 1)

# Convert to long format for ggplot2
log2_df <- as.data.frame(norm_log_counts)
log2_df$Gene <- rownames(log2_df)
log2_long <- pivot_longer(log2_df, -Gene, names_to = "Sample", values_to = "log10_count")

# Plot with ggplot2
p1 = ggplot(log1_long, aes(x = log10_count, color = Sample)) +
  geom_density(size = 1.2, alpha = 0.8) +
  theme_minimal(base_size = 14) +
  labs(
    title = "Density Plot of log10 Raw Counts (Before Normalization)",
    x = "log10(Counts + 1)",
    y = "Density"
  ) +
  theme(
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10),
    legend.position = "right"
  )
# Plot with ggplot2
p2 = ggplot(log2_long, aes(x = log10_count, color = Sample)) +
  geom_density(size = 1.2, alpha = 0.8) +
  theme_minimal(base_size = 14) +
  labs(
    title = "Density Plot of log10 Normalized Counts",
    x = "log10(Normalized Counts + 1)",
    y = "Density"
  ) +
  theme(
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10),
    legend.position = "right"
  )

# Combine side-by-side
options(repr.plot.width = 14, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')

Warning message:
"Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead."

print("printing the results")
resultsNames(dds)

[1] "printing the results"

# Show the colData table (all sample metadata)
colData(dds)

# View just the 'condition' column
dds$condition

# Count samples per condition
table(dds$condition)

# Show levels (if it's a factor)
levels(dds$condition)

DataFrame with 6 rows and 2 columns
        condition sizeFactor
         <factor>  <numeric>
CS13_1      CS13    0.878990
CS13_2      CS13    0.854727
DC13_1      DC13    0.929657
DC13_2      DC13    1.072809
pla13_1     pla13   0.840499
pla13_2     pla13   1.684889

pla13  CS13  DC13 
    2     2     2

levels(dds$condition)
# [1] "pla13" "CS13" "DC13"

print("No shrinkage")

# Get results for different comparisons
res_CS13_vs_DC13 <- results(dds, contrast = c("condition", "CS13", "DC13"))
res_CS13_vs_pla13 <- results(dds, contrast = c("condition", "CS13", "pla13"))
res_DC13_vs_pla13 <- results(dds, contrast = c("condition", "DC13", "pla13"))

# summary(res_CS13_vs_DC13)
# summary(res_DC13_vs_pla13)
# summary(res_CS13_vs_pla13)

# Spla13e results
write.csv(as.data.frame(res_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.csv")
write.csv(as.data.frame(res_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.csv")
write.csv(as.data.frame(res_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.csv")

###########################################################
###########################################################

print("number of differentially bound transcripts : CS13 vs DC13 : pvalue < 0.05 and padj < 0.1")
dim(subset(res_CS13_vs_DC13, pvalue < 0.05))[1]
dim(subset(res_CS13_vs_DC13, padj < 0.1))[1]

print("number of differentially bound transcripts : CS13 vs pla13 : pvalue < 0.05 and padj < 0.1")
dim(subset(res_CS13_vs_pla13, pvalue < 0.05))[1]
dim(subset(res_CS13_vs_pla13, padj < 0.1))[1]

print("number of differentially bound transcripts : DC13 vs pla13 : pvalue < 0.05 and padj < 0.1")
dim(subset(res_DC13_vs_pla13, pvalue < 0.05))[1]
dim(subset(res_DC13_vs_pla13, padj < 0.1))[1]

###########################################################
###########################################################

[1] "No shrinkage"
[1] "number of differentially bound transcripts : CS13 vs DC13 : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound transcripts : CS13 vs pla13 : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound transcripts : DC13 vs pla13 : pvalue < 0.05 and padj < 0.1"

cat("Available contrasts in resultsNames(dds):\n")
print(resultsNames(dds))

Available contrasts in resultsNames(dds):
[1] "Intercept"               "condition_CS13_vs_pla13"
[3] "condition_DC13_vs_pla13"

# type = c("apeglm", "ashr", "normal")

print("Data shrinkage : normal lfcShrink")

[1] "Data shrinkage : normal lfcShrink"

# Get results for different comparisons

resLFCnormal_CS13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "CS13", "pla13"), type="normal")
resLFCnormal_DC13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "DC13", "pla13"), type="normal")
resLFCnormal_CS13_vs_DC13 <- lfcShrink(dds, contrast = c("condition", "CS13", "DC13"), type="normal")

# summary(resLFCnormal_CS13_vs_DC13)
# summary(resLFCnormal_DC13_vs_pla13)
# summary(resLFCnormal_CS13_vs_pla13)

# Spla13e results
write.csv(as.data.frame(resLFCnormal_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.resLFCnormal.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCnormal: CS13 vs DC13 : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_CS13_vs_DC13, pvalue < 0.05))
dim(subset(resLFCnormal_CS13_vs_DC13, padj < 0.1))

print("number of differentially bound and expressed transcripts : resLFCnormal : CS13 vs pla13 : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_CS13_vs_pla13, pvalue < 0.05))
dim(subset(resLFCnormal_CS13_vs_pla13, padj < 0.1))

print("number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_DC13_vs_pla13, pvalue < 0.05))
dim(subset(resLFCnormal_DC13_vs_pla13, padj < 0.1))

###########################################################
###########################################################

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

[1] "number of differentially bound and expressed transcripts : resLFCnormal: CS13 vs DC13 : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : resLFCnormal : CS13 vs pla13 : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05 and padj < 0.1"

print("Data shrinkage : ashr lfcShrink")

[1] "Data shrinkage : ashr lfcShrink"

# If you must use contrast, you should use type="normal" or type="ashr" instead of apeglm, 
# because apeglm only works with coef.  
# apeglm is the recommended method for log-fold change shrinkage.

# Get results for different comparisons
# resLFCCS13eglm_CS13_vs_pla13 <- lfcShrink(dds, coef = "condition_CS13_vs_pla13", type="CS13eglm")
# resLFCCS13eglm_DC13_vs_pla13 <- lfcShrink(dds, coef = "condition_DC13_vs_pla13", type="CS13eglm")

resLFCashr_CS13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "CS13", "pla13"), type="ashr")
resLFCashr_DC13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "DC13", "pla13"), type="ashr")
resLFCashr_CS13_vs_DC13 <- lfcShrink(dds, contrast = c("condition", "CS13", "DC13"), type="ashr")

# summary(resLFCashr_CS13_vs_DC13)
# summary(resLFCashr_DC13_vs_pla13)
# summary(resLFCashr_CS13_vs_pla13)

# Spla13e results
write.csv(as.data.frame(resLFCashr_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.resLFCashr.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCashr : CS13 vs DC13 : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_CS13_vs_DC13, pvalue < 0.05))[1]
dim(subset(resLFCashr_CS13_vs_DC13, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : CS13 vs pla13 : pvalue < 0.05 ")
dim(subset(resLFCashr_CS13_vs_pla13, pvalue < 0.05))[1]
dim(subset(resLFCashr_CS13_vs_pla13, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05")
dim(subset(resLFCashr_DC13_vs_pla13, pvalue < 0.05))[1]
dim(subset(resLFCashr_DC13_vs_pla13, padj < 0.1))[1]

###########################################################
###########################################################

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

[1] "number of differentially bound and expressed transcripts : resLFCashr : CS13 vs DC13 : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : CS13 vs pla13 : pvalue < 0.05 "

[1] "number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05"

# If you must use contrast, you should use type="normal" or type="ashr" instead of CS13eglm, 
# because CS13eglm only works with coef.  
# CS13eglm is the recommended method for log-fold change shrinkage.

# Get results for different comparisons
# resLFCCS13eglm_CS13_vs_pla13 <- lfcShrink(dds, coef = "condition_CS13_vs_pla13", type="CS13eglm")
# resLFCCS13eglm_DC13_vs_pla13 <- lfcShrink(dds, coef = "condition_DC13_vs_pla13", type="CS13eglm")

resLFCashr_CS13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "CS13", "pla13"), type="ashr")
resLFCashr_DC13_vs_pla13 <- lfcShrink(dds, contrast = c("condition", "DC13", "pla13"), type="ashr")
resLFCashr_CS13_vs_DC13 <- lfcShrink(dds, contrast = c("condition", "CS13", "DC13"), type="ashr")

# summary(resLFCashr_CS13_vs_DC13)
# summary(resLFCashr_DC13_vs_pla13)
# summary(resLFCashr_CS13_vs_pla13)

# Spla13e results
write.csv(as.data.frame(resLFCashr_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.resLFCashr.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCashr : CS13 vs DC13 : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_CS13_vs_DC13, pvalue < 0.05))[1]
dim(subset(resLFCashr_CS13_vs_DC13, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : CS13 vs pla13 : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_CS13_vs_pla13, pvalue < 0.05))[1]
dim(subset(resLFCashr_CS13_vs_pla13, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_DC13_vs_pla13, pvalue < 0.05))[1]
dim(subset(resLFCashr_DC13_vs_pla13, padj < 0.1))[1]

###########################################################
###########################################################

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

[1] "number of differentially bound and expressed transcripts : resLFCashr : CS13 vs DC13 : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : CS13 vs pla13 : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : DC13 vs pla13 : pvalue < 0.05 and padj < 1"

print("Comparing the number of DE genes for the comparison : CS13 vs DC13 for a pvalue < 0.05")

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

# The information about DE peaks was stored in :
# res_CS13_vs_DC13 
# res_CS13_vs_pla13 
# res_DC13_vs_pla13 

# resLFCnormal_CS13_vs_pla13
# resLFCnormal_DC13_vs_pla13 
# resLFCnormal_CS13_vs_DC13 

# resLFCashr_CS13_vs_pla13 
# resLFCashr_DC13_vs_pla13 
# resLFCashr_CS13_vs_DC13 

# Count DEGs
n_DE_unshrunken <- sum(res_CS13_vs_DC13$pvalue < 0.05, na.rm = TRUE)
n_DE_shrink_normal <- sum(resLFCnormal_CS13_vs_DC13$pvalue < 0.05, na.rm = TRUE)  # Same p-values as unshrunken
n_DE_ashr <- sum( resLFCashr_CS13_vs_DC13$pvalue < 0.05, na.rm = TRUE)            # Same p-values as unshrunken

# Build a data frame
compare_df1 <- data.frame(
  Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
  DE_Genes = c(n_DE_unshrunken, n_DE_shrink_normal, n_DE_ashr)
)

# Plot it
p1 = ggplot(compare_df1, aes(x = Method, y = DE_Genes, fill = Method)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(
    title = "Comparison of DE Gene Counts (DC13 vs CS13)",
    y = "Number of DE Genes (p < 0.05 or s < 0.05)",
    x = "Shrinkage Method"
  ) +
  theme(legend.position = "none") +
  geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4)

print("Comparing the number of DE genes for the comparison : CS13 vs DC13 for a pvalue < 0.05")

# Shrinkage Methods: The lfcShrink() function in DESeq2 is used to obtain more DC13curate estimates of log2 fold changes, 
# especially for genes with low counts or high variability.
# Threshold Selection: The choice of a log2FC threshold (e.g., 0.3) is somewhat arbitrary and should be based on the biological context 
# and the desired stringency of the analysis.
# Interpretation: Comparing the number of DE genes DC13ross different shrinkage methods can provide insights into the robustness of your findings. 
# It's common to observe variations in the number of DE genes identified, depending on the method used.

# Raw (non-shrunk)
n_raw <- sum(res_CS13_vs_DC13$pvalue < pval_cutoff & abs(res_CS13_vs_DC13$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Normal shrink
n_normal <- sum(res_CS13_vs_DC13$pvalue < pval_cutoff & abs(resLFCnormal_CS13_vs_DC13$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Ashr shrink (using s-value instead of p-value)
n_ashr <- sum(resLFCashr_CS13_vs_DC13$pvalue < pval_cutoff & abs(resLFCashr_CS13_vs_DC13$log2FoldChange) > lfc_cutoff, na.rm = TRUE)

# Combine into a data frame
compare_df2 <- data.frame(
  Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
  DE_Genes = c(n_raw, n_normal, n_ashr)
)

p2 = ggplot(compare_df2, aes(x = Method, y = DE_Genes, fill = Method)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(
    title = "DE Gene Counts (CS13 vs DC13)",
    subtitle = "p < 0.05 and |log2FC| > 0.5",
    y = "Number of DE Genes",
    x = "Method"
  ) +
  geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4) +
  theme(legend.position = "none")


# Print the plot in Jupyter
options(repr.plot.width = 8, repr.plot.height = 6)
p1 + p2

[1] "Comparing the number of DE genes for the comparison : CS13 vs DC13 for a pvalue < 0.05"
[1] "Comparing the number of DE genes for the comparison : CS13 vs DC13 for a pvalue < 0.05"

print("MA plots:")

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

[1] "MA plots:"

make_MA_plot <- function(res_df, title = "MA Plot", lfc_cutoff = 0.3, pval_cutoff = 0.1, ylim = c(-2, 2)) {
  
  res_df <- as.data.frame(res_df)
  
  # ReplDC13e NA p-values with the threshold so they are not considered significant
  res_df$pvalue[is.na(res_df$pvalue)] <- 1
  
  # Label significance based on thresholds
  res_df$sig <- ifelse(res_df$pvalue < pval_cutoff & abs(res_df$log2FoldChange) > lfc_cutoff,
                       "Significant", "Not Significant")

  # Generate the MA plot
  ggplot(res_df, aes(x = baseMean, y = log2FoldChange, color = sig)) +
    geom_point(alpha = 0.6, size = 1) +
    scale_x_log10() +
    scale_color_manual(values = c("Significant" = "#D7263D", "Not Significant" = "gray70")) +
    geom_hline(yintercept = c(-lfc_cutoff, lfc_cutoff), linetype = "dashed", color = "black") +
    coord_cartesian(ylim = ylim) +
    theme_minimal(base_size = 14) +
    labs(
      title = title,
      x = "Mean Expression (log10 scale)",
      y = "log2 Fold Change",
      color = "Significance"
    ) +
    theme(
      legend.position = "right",
      panel.grid.minor = element_blank()
    )
}

print("MA plots:")
print("CS13 vs DC13")

# Create the plots
p1 <- make_MA_plot(res_CS13_vs_DC13, title = "CS13 vs DC13 (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_CS13_vs_DC13, title = "CS13 vs DC13 (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_CS13_vs_DC13, title = "CS13 vs DC13 (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "CS13 vs DC13"

print("MA plots:")
print("DC13 vs pla13")

# Create the plots
p1 <- make_MA_plot(res_DC13_vs_pla13, title = "DC13 vs pla13 (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_DC13_vs_pla13, title = "DC13 vs pla13 (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_DC13_vs_pla13, title = "DC13 vs pla13 (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "DC13 vs pla13"

print("MA plots:")
print("CS13 vs pla13")

# Create the plots
p1 <- make_MA_plot(res_CS13_vs_pla13, title = "CS13 vs pla13 (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_CS13_vs_pla13, title = "CS13 vs pla13 (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_CS13_vs_pla13, title = "CS13 vs pla13 (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "CS13 vs pla13"

print("plotting dispersion")

# Combine side-by-side
options(repr.plot.width = 6, repr.plot.height = 6)

plotDispEsts(dds)
dispersionFunction(dds)

[1] "plotting dispersion"

structure(function (q) 
coefs[1] + coefs[2]/q, coefficients = c(asymptDisp = 0.0180702544543159, 
extraPois = 3.78232368735803), fitType = "parametric", varLogDispEsts = 0.887953522627589, dispPriorVar = 0.25)

print("RLD and VST transformations")

# Effects of transformations on the variance
rld <- rlog(dds, blind = FALSE)  
vsd <- vst(dds, blind = FALSE) 
ntd <- normTransform(dds)
# meanSdPlot(assay(ntd))
# meanSdPlot(assay(rld))
# meanSdPlot(assay(vsd))

[1] "RLD and VST transformations"

library("pheatmap")

# Select the top 20 differentially expressed genes based on adjusted p-value
top_genes <- rownames(res_CS13_vs_DC13)[order(res_CS13_vs_DC13$padj, na.last=NA)][1:20]  #

# ExtrDC13t normalized transformed counts for the top genes
top_counts <- assay(vsd)[top_genes, ]

# Create annotation dataframe
df <- as.data.frame(colData(dds)["condition"])  # Ensure it is a proper dataframe
colnames(df) <- "Condition"  # Rename column for clarity

# Generate heatmCS13
options(repr.plot.width = 6, repr.plot.height = 6)
pheatmap(top_counts, 
         cluster_rows=TRUE,  # Cluster rows to group similar genes
         show_rownames=TRUE,  # Show gene names
         cluster_cols=TRUE,  # Cluster samples
         annotation_col=df,  # Add sample condition annotations
         scale="row",  # Normalize eDC13h gene's expression DC13ross samples
         fontsize_row=8)  # Adjust row text size for readability

print("PCA and MDS plots of rlog- and vst-transformed counts")

[1] "PCA and MDS plots of rlog- and vst-transformed counts"

# 1. rlog transformation and PCA
rld <- rlog(dds, blind = FALSE)
pca_rld <- plotPCA(rld, intgroup = "condition", returnData = TRUE)
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

pca_rld_plot <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  xlab(paste0("PC1: ", percentVar_rld[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_rld[2], "% variance")) +
  ggtitle("PCA (rlog)") +
  theme_minimal()

# 2. rlog MDS
dists_rld <- dist(t(assay(rld)))
mds_rld <- cmdscale(as.matrix(dists_rld))
mds_rld_df <- data.frame(MDS1 = mds_rld[,1], MDS2 = mds_rld[,2], condition = col_data$condition)

mds_rld_plot <- ggplot(mds_rld_df, aes(MDS1, MDS2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  ggtitle("MDS (rlog)") +
  theme_minimal()

# 3. vst transformation and PCA
vsd <- vst(dds, blind = FALSE)
pca_vsd <- plotPCA(vsd, intgroup = "condition", returnData = TRUE)
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))

pca_vsd_plot <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  xlab(paste0("PC1: ", percentVar_vsd[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_vsd[2], "% variance")) +
  ggtitle("PCA (vst)") +
  theme_minimal()

# 4. vst MDS
dists_vsd <- dist(t(assay(vsd)))
mds_vsd <- cmdscale(as.matrix(dists_vsd))
mds_vsd_df <- data.frame(MDS1 = mds_vsd[,1], MDS2 = mds_vsd[,2], condition = col_data$condition)

mds_vsd_plot <- ggplot(mds_vsd_df, aes(MDS1, MDS2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  ggtitle("MDS (vst)") +
  theme_minimal()

# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 8)
# Combine all plots in a 2x2 grid
plot_grid(
  pca_rld_plot, mds_rld_plot,
  pca_vsd_plot, mds_vsd_plot,
  labels = c("A", "B", "C", "D"),
  ncol = 2, align = "hv"
)

using ntop=500 top features by variance

using ntop=500 top features by variance

library(pheatmap)
library(RColorBrewer)
library(gridExtra)
library(grid)

# === RLOG HeatmCS13 ===
rlog_matrix <- assay(rld)
sampleDists_rlog <- dist(t(rlog_matrix))
sampleDistMatrix_rlog <- as.matrix(sampleDists_rlog)
colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)

p1 <- pheatmap(sampleDistMatrix_rlog,
               clustering_distance_rows = sampleDists_rlog,
               clustering_distance_cols = sampleDists_rlog,
               col = colors,
               fontsize_row = 10,
               fontsize_col = 10,
               cellwidth = 60,
               cellheight = 60,
               angle_col = 45,
               main = "Sample Distance Heatmap (rlog)",
               silent = TRUE)

# === VST HeatmCS13 ===
vsd_matrix <- assay(vsd)
sampleDists_vsd <- dist(t(vsd_matrix))
sampleDistMatrix_vsd <- as.matrix(sampleDists_vsd)

p2 <- pheatmap(sampleDistMatrix_vsd,
               clustering_distance_rows = sampleDists_vsd,
               clustering_distance_cols = sampleDists_vsd,
               col = colors,
               fontsize_row = 10,
               fontsize_col = 10,
               cellwidth = 60,
               cellheight = 60,
               angle_col = 45,
               main = "Sample Distance Heatmap (vst)",
               silent = TRUE)

# === Combine with spDC13ing and ensure layout fits ===
grid.newpage()  # Ensures fresh drawing surfDC13e

# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 10)

# === Convert pheatmCS13 outputs to grobs ===
grob1 <- p1[[4]]
grob2 <- p2[[4]]

# === Combine with cowplot ===
cowplot::plot_grid(grob1, grob2, ncol = 2, rel_widths = c(1, 1))

Attaching package: 'gridExtra'


The following object is masked from 'package:dplyr':

    combine


The following object is masked from 'package:Biobase':

    combine


The following object is masked from 'package:BiocGenerics':

    combine

print("Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances")
# Another option for calculating sample distances is to use the Poisson Distance (Witten 2011), implemented in the PoiClDC13lu pDC13kage.
# This measure of dissimilarity between counts also takes the inherent variance structure of counts into consideration when calculating
# the distances between samples. The PoissonDistance function takes the original count matrix (not normalized) with samples as rows
# instead of columns, so we need to transpose the counts in dds.

[1] "Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances"

print("PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale().")

# === GLM-PCA plot ===
gpca <- glmpca(assay(dds), L = 2)
gpca.dat <- gpca$factors
gpca.dat$sample <- colnames(dds)
gpca.dat$condition <- colData(dds)$condition

p_gpca <- ggplot(gpca.dat, aes(x = dim1, y = dim2, color = condition)) +
  geom_point(size = 4.5, alpha = 0.85, stroke = 1) +
  geom_text_repel(aes(label = sample), size = 4, box.padding = 0.4, max.overlaps = 8) +
  coord_fixed() +
  theme_minimal(base_size = 16) +
  labs(title = "GLM-PCA", x = "GLM-PC1", y = "GLM-PC2") +
  scale_color_brewer(palette = "Set2") +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold", size = 16)
  )

# === Poisson distance heatmap ===
poisd <- PoissonDistance(t(counts(dds)))
samplePoisDistMatrix <- as.matrix(poisd$dd)

sample_names <- colnames(dds)
rownames(samplePoisDistMatrix) <- sample_names
colnames(samplePoisDistMatrix) <- sample_names

colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)

pheat <- pheatmap(samplePoisDistMatrix,
                  clustering_distance_rows = poisd$dd,
                  clustering_distance_cols = poisd$dd,
                  col = colors,
                  fontsize_row = 10,
                  fontsize_col = 10,
                  cellwidth = 50,
                  cellheight = 50,
                  angle_col = 45,
                  main = "Poisson Distance Heatmap",
                  silent = TRUE)

# Convert to grob for use with cowplot
g_poisson <- ggdraw(grobTree(pheat$gtable)) + theme(plot.margin = margin(5, 5, 5, 5))

# Set display size
options(repr.plot.width = 16, repr.plot.height = 8)

# Combine plots with cleaner spacing
combined_plot <- plot_grid(
  g_poisson, p_gpca,
  labels = c("A", "B"),
  label_size = 16,
  nrow = 1,
  rel_widths = c(1.2, 1)
)

# Print
print(combined_plot)

[1] "PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale()."

print("Performing Surrogate Variable Analysis")
print("SVA analysis")
# SV1, SV2, ... are surrogate variables — latent (hidden) fDC13tors estimated from the data that cCS13ture unwanted variation 
# (like batch effects, technical noise, or hidden biological subtypes).
# You can think of them as "virtual covariates" — constructed purely from the structure of your data — 
# that explain sources of variation not included in your model (like treatment or condition).

[1] "Performing Surrogate Variable Analysis"
[1] "SVA analysis"

print("If you see perfect correlation (1 or -1) between any two columns, 
       your design is collinear → and SVA breaks.")

[1] "If you see perfect correlation (1 or -1) between any two columns, \n       your design is collinear → and SVA breaks."

# === Prepare data ===
# dat  <- counts(dds, normalized = TRUE)
# idx  <- rowMeans(dat) > 1
# dat  <- dat[idx, ]

# mod  <- model.matrix(~ condition, colData(dds))
# mod0 <- model.matrix(~ 1, colData(dds))

# svseq <- svaseq(dat, mod, mod0, n.sv = 1)
# head(svseq$sv, 2)

# Set layout: 1 row, 2 columns
# par(
#  mfrow = c(1, 2),
#  mar = c(5, 5, 4, 2) + 0.1,  # bottom, left, top, right margins
#  cex.main = 1.4,             # title size
#  cex.axis = 1.1,             # axis label size
#  cex.lab = 1.2,              # axis title size
#  las = 1                     # y-axis labels horizontal
# )

# Loop through SV1 and SV2
# for (i in 1:2) {
#  stripchart(
#    svseq$sv[, i] ~ dds$condition,
#    vertical = TRUE,
#    method = "jitter",
#    pch = 21,
#    bg = "steelblue",
#    col = "black",
#    frame.plot = FALSE,
#    ylim = c(-0.8, 0.8),  # fixed y-axis range
#    main = paste0("Surrogate Variable SV", i),
#    ylab = "Surrogate Variable Value",
#    xlab = "Condition",
#    cex = 1.3
#  )
#  abline(h = 0, lty = 2, col = "gray50", lwd = 1.5)
# }

# qr(mod)$rank
# ncol(mod)
# cor(mod)

# Finally, in order to use SVA to remove any effect on the counts from our surrogate variables, we simply add these two surrogate variables 
# as columns to the DESeqDataSet and then add them to the design:

# ddssva <- dds
# ddssva$SV1 <- svseq$sv[,1]
# ddssva$SV2 <- svseq$sv[,2]
# design(ddssva) <- ~ SV1 + SV2 + condition
  
# ddssva$SV1
# ddssva$SV2

# length(ddssva$SV1)
# length(ddssva$SV2)

# ddssva <- DESeq(ddssva)
# resultsNames(ddssva)

# rowRanges(ddssva)
# colData(ddssva)
# assays(ddssva)
# assay(ddssva)
# length(rowRanges(ddssva))

# res_ddssva <- results(ddssva)
# resultsNames(res_ddssva)

# Get results for different comparisons
# res_ddssva_CS13_vs_DC13 <- results(ddssva, contrast = c("condition", "CS13", "DC13"))
# res_ddssva_CS13_vs_pla13 <- results(ddssva, contrast = c("condition", "CS13", "pla13"))
# res_ddssva_DC13_vs_pla13 <- results(ddssva, contrast = c("condition", "DC13", "pla13"))

# summary(res_ddssva_CS13_vs_pla13)
# summary(res_ddssva_DC13_vs_pla13)
# summary(res_ddssva_CS13_vs_DC13)

# Spla13e results
# write.csv(as.data.frame(res_ddssva_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.sva.csv")
# write.csv(as.data.frame(res_ddssva_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.sva.csv")
# write.csv(as.data.frame(res_ddssva_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.sva.csv")

###########################################################
###########################################################

# print("number of differentially bound and expressed transcripts : CS13 vs DC13 : pvalue < 0.05, and padj < 0.1")
# dim(subset(res_ddssva_CS13_vs_DC13, pvalue < 0.05))
# dim(subset(res_ddssva_CS13_vs_DC13, padj < 0.1))

# print("number of differentially bound and expressed transcripts : CS13 vs pla13 : pvalue < 0.05, and padj < 0.1")
# dim(subset(res_ddssva_CS13_vs_pla13, pvalue < 0.05))
# dim(subset(res_ddssva_CS13_vs_pla13, padj < 0.1))

# print("number of differentially bound and expressed transcripts : DC13 vs pla13 : pvalue < 0.05, and padj < 0.1")
# dim(subset(res_ddssva_DC13_vs_pla13, pvalue < 0.05))
# dim(subset(res_ddssva_DC13_vs_pla13, padj < 0.1))

# Transform count data
# vsd2 <- vst(ddssva, blind = TRUE)
# rld2 <- rlog(ddssva, blind = TRUE)

# Get PCA data
# pca_vsd <- plotPCA(vsd2, intgroup = "condition", returnData = TRUE)
# pca_rld <- plotPCA(rld2, intgroup = "condition", returnData = TRUE)

# Variance explained
# percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
# percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

# PCA plot for VST
# p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
#  geom_point(size = 3, alpha = 0.8) +
#  labs(
#    title = "PCA after SVA (VST)",
#    x = paste0("PC1 (", percentVar_vsd[1], "%)"),
#    y = paste0("PC2 (", percentVar_vsd[2], "%)")
#  ) +
#  theme_minimal(base_size = 14) +
#  scale_color_brewer(palette = "Set2") +
#  theme(legend.position = "right")

# PCA plot for RLOG
# p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
#  geom_point(size = 3, alpha = 0.8) +
#  labs(
#    title = "PCA after SVA (RLOG)",
#    x = paste0("PC1 (", percentVar_rld[1], "%)"),
#    y = paste0("PC2 (", percentVar_rld[2], "%)")
#  ) +
#  theme_minimal(base_size = 14) +
#  scale_color_brewer(palette = "Set2") +
#  theme(legend.position = "right")

# Show both plots side by side with legends
# options(repr.plot.width = 14, repr.plot.height = 6)
# plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)

print("RUVseq analysis")

[1] "RUVseq analysis"

library(RUVSeq)
library(DESeq2)

# Create SeqExpressionSet from DESeq2 object
set <- newSeqExpressionSet(counts(dds))

# Keep genes with sufficient expression
idx  <- rowSums(counts(set) > 5) >= 2
set  <- set[idx, ]

# Normalize
set <- betweenLaneNormalization(set, which = "upper")

# Run DESeq2 just to get raw p-values for empirical control genes
dds_temp <- dds[idx, ]
dds_temp <- DESeq(dds_temp)
res_temp <- results(dds_temp)

# Define empirical control genes as those with high p-value (non-DE)
not.sig <- rownames(res_temp)[which(res_temp$pvalue > 0.1)]
empirical <- rownames(set)[rownames(set) %in% not.sig]

# CS13ply RUVg with k=2 unwanted fDC13tors
set <- RUVg(set, empirical, k = 2)

# Add unwanted fDC13tors to DESeq2 design
ddsruv <- dds[idx, ]  # use filtered genes
ddsruv$W1 <- set$W_1
ddsruv$W2 <- set$W_2
design(ddsruv) <- ~ W1 + W2 + condition

# Run DESeq2 with adjusted design
ddsruv <- DESeq(ddsruv)

# Check model variables
resultsNames(ddsruv)

# Get results for different comparisons
res_ddsruv_CS13_vs_DC13 <- results(ddsruv, contrast = c("condition", "CS13", "DC13"))
res_ddsruv_CS13_vs_pla13 <- results(ddsruv, contrast = c("condition", "CS13", "pla13"))
res_ddsruv_DC13_vs_pla13 <- results(ddsruv, contrast = c("condition", "DC13", "pla13"))

# Spla13e results
write.csv(as.data.frame(res_ddsruv_CS13_vs_DC13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_DC13_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_CS13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_CS13_vs_pla13_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_DC13_vs_pla13), file = "A2780.peaks.IP.samples.DESeq2_DC13_vs_pla13_results.ruv.csv")

# Summary statistics
print("number of differentially bound and expressed transcripts : CS13 vs DC13 : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_CS13_vs_DC13, pvalue < 0.05)))
print(dim(subset(res_ddsruv_CS13_vs_DC13, padj < 0.1)))

print("number of differentially bound and expressed transcripts : CS13 vs pla13 : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_CS13_vs_pla13, pvalue < 0.05)))
print(dim(subset(res_ddsruv_CS13_vs_pla13, padj < 0.1)))

print("number of differentially bound and expressed transcripts : DC13 vs pla13 : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_DC13_vs_pla13, pvalue < 0.05)))
print(dim(subset(res_ddsruv_DC13_vs_pla13, padj < 0.1)))

using pre-existing size factors

estimating dispersions

found already estimated dispersions, replacing these

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

using pre-existing size factors

estimating dispersions

found already estimated dispersions, replacing these

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

[1] "number of differentially bound and expressed transcripts : CS13 vs DC13 : pvalue < 0.05, and padj < 0.1"
[1] 132   6
[1] 1 6
[1] "number of differentially bound and expressed transcripts : CS13 vs pla13 : pvalue < 0.05, and padj < 0.1"
[1] 85  6
[1] 0 6
[1] "number of differentially bound and expressed transcripts : DC13 vs pla13 : pvalue < 0.05, and padj < 0.1"
[1] 269   6
[1] 29  6

# Set layout to 1 row, 2 columns (side by side)
par(
  mfrow = c(1, 2),
  mar = c(4, 4, 3, 1),   # margins: bottom, left, top, right
  cex.main = 1.2,        # title size
  cex.axis = 1.0,        # axis tick label size
  cex.lab = 1.1,         # axis title size
  las = 1                # horizontal y-axis labels
)

# Loop over W1 and W2
for (i in 1:2) {
  stripchart(
    pData(set)[, i] ~ dds$condition,
    vertical = TRUE,
    method = "jitter",
    pch = 21,
    bg = "steelblue",
    col = "black",
    frame.plot = FALSE,
    main = paste("W", i),
    ylab = "FDC13tor Value",
    xlab = "Condition",
    cex = 1.1
  )
  abline(h = 0, lty = 2, col = "gray60", lwd = 1)
}

# Transform count data from ddsruv
vsd3 <- vst(ddsruv, blind = TRUE)
rld3 <- rlog(ddsruv, blind = TRUE)

# Get PCA data
pca_vsd <- plotPCA(vsd3, intgroup = "condition", returnData = TRUE)
pca_rld <- plotPCA(rld3, intgroup = "condition", returnData = TRUE)

# Variance explained
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

# PCA plot for VST
p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after RUV (VST)",
    x = paste0("PC1 (", percentVar_vsd[1], "%)"),
    y = paste0("PC2 (", percentVar_vsd[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# PCA plot for RLOG
p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after RUV (RLOG)",
    x = paste0("PC1 (", percentVar_rld[1], "%)"),
    y = paste0("PC2 (", percentVar_rld[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# Show both plots side by side with legends
options(repr.plot.width = 14, repr.plot.height = 6)
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)

using ntop=500 top features by variance

using ntop=500 top features by variance

library(EnhancedVolcano)

# Color	Label in legend	Meaning
# Grey	NS	Not Significant – the gene did not pass the p-value or log₂FC thresholds
# Green	Log₂ FC	The gene passed the log₂ fold change cutoff but not the p-value cutoff
# Blue	p-value	The gene passed the p-value cutoff but not the log₂FC cutoff
# Red	p value and log₂ FC	The gene passed both p-value and log₂FC thresholds — most interesting hits

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

# Set up the plotting window size for a more compDC13t layout
options(repr.plot.width = 10, repr.plot.height = 8)

EnhancedVolcano(res_CS13_vs_DC13,
                lab = rownames(res_CS13_vs_DC13),
                x = 'log2FoldChange',
                y = 'pvalue',
                pCutoff = pval_cutoff,
                FCcutoff = lfc_cutoff,
                title = 'CS13 vs DC13',
                pointSize = 2.0,
                labSize = 3.0,
                legendPosition = 'right',
                legendLabSize = 10,
                legendIconSize = 3.0,
                drawConnectors = TRUE,
                widthConnectors = 0.5,
                boxedLabels = FALSE)

Warning message:
"ggrepel: 676 unlabeled data points (too many overlaps). Consider increasing max.overlaps"

library(clusterProfiler)
library(org.Hs.eg.db) 
library(GO.db)         
library(DO.db)         
library(KEGGREST)      
library(ReactomePA)    
library(enrichplot)    
library(dplyr)
library(msigdbr)
library(msigdb)
library(msigdf)
library(msigdbdf)

clusterProfiler v4.14.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

G Yu. Thirteen years of clusterProfiler. The Innovation. 2024,
5(6):100722


Attaching package: 'clusterProfiler'


The following objects are masked from 'package:ensembldb':

    filter, select


The following object is masked from 'package:biomaRt':

    select


The following object is masked from 'package:AnnotationDbi':

    select


The following object is masked from 'package:XVector':

    slice


The following object is masked from 'package:purrr':

    simplify


The following object is masked from 'package:IRanges':

    slice


The following object is masked from 'package:S4Vectors':

    rename


The following object is masked from 'package:stats':

    filter


ReactomePA v1.50.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
reactome pathway analysis and visualization. Molecular BioSystems.
2016, 12(2):477-479

enrichplot v1.26.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu and Shengqi
Wang. GOSemSim: an R package for measuring semantic similarity among GO
terms and gene products. Bioinformatics. 2010, 26(7):976-978

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
fin_name = "A2780.peaks.IP.samples."

res <- res_CS13_vs_DC13  
head(res,3)

log2 fold change (MLE): condition CS13 vs DC13 
Wald test p-value: condition CS13 vs DC13 
DataFrame with 3 rows and 6 columns
                 baseMean log2FoldChange     lfcSE      stat    pvalue
                <numeric>      <numeric> <numeric> <numeric> <numeric>
ENSG00000290825   24.0955      -0.632237  0.667491 -0.947183  0.343545
ENSG00000310526  168.3973      -0.244664  0.311804 -0.784674  0.432645
ENSG00000241860   24.1525      -0.389107  0.757737 -0.513512  0.607593
                     padj
                <numeric>
ENSG00000290825        NA
ENSG00000310526  0.790866
ENSG00000241860        NA

# Filter significant genes
res_sig <- as.data.frame(res) %>%
  rownames_to_column("gene") %>%
  filter(padj < pval_cutoff & abs(log2FoldChange) > lfc_cutoff)

# MCS13 ENSEMBL IDs to Entrez IDs
gene_ids <- bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
res_merge <- merge(res_sig, gene_ids, by.x = "gene", by.y = "ENSEMBL")

# Prepare named gene list for GSEA
gene_list2 <- setNames(res_merge$log2FoldChange, res_merge$ENTREZID)
gene_list2 <- sort(gene_list2, decreasing = TRUE)

head(gene_list2, 2)
length(gene_list2)

'select()' returned 1:many mapping between keys and columns

Warning message in bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", :
"15.84% of input gene IDs are fail to map..."

library(clusterProfiler)
library(org.Hs.eg.db)
library(cowplot)

# Predefine result objects to pla13oid "not found" error
result <- NULL
result2 <- NULL

# GO Over-Representation Analysis (ORA)
result <- tryCatch({
  ego <- enrichGO(gene          = gene_ids$ENTREZID,
                  OrgDb         = org.Hs.eg.db,
                  ont           = "BP",
                  keyType       = "ENTREZID",  
                  pAdjustMethod = "BH",
                  pvalueCutoff  = 0.05,
                  qvalueCutoff  = 1,
                  readable      = TRUE)
  
  # Spla13e results
  write.table(ego@result, file = paste0(fin_name, "_GO_OverRepresentation_Results.txt"), 
              row.names = FALSE, col.names = TRUE, quote = FALSE)

  # Spla13e PNG
  png(paste0(fin_name, "_GO_OverRepresentation.png"), width = 1000, height = 800)
  print(dotplot(ego, showCategory = 20, title = "GO ORA (BP)"))
  dev.off()

  # Return ggplot object
  dotplot(ego, showCategory = 20, title = "GO ORA (BP)")

}, error = function(e) {
  cat("Error in GO ORA:", conditionMessage(e), "\n")
  NULL
})


# GO Enrichment Analysis (GSEA)
result2 <- tryCatch({
  ego2 <- gseGO(gene          = gene_list2,
                OrgDb         = org.Hs.eg.db,
                keyType       = "ENTREZID",  
                ont           = "BP",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.05)

  if (nrow(ego2@result) > 0) {
    write.table(ego2@result, file = paste0(fin_name, "_GO_Enrichment_Results.txt"), 
                row.names = FALSE, col.names = TRUE, quote = FALSE)

    png(paste0(fin_name, "_GO_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)"))
    dev.off()

    dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)")
  } else {
    cat("No enriched terms in GSEA under pvalueCutoff.\n")
    NULL
  }

}, error = function(e) {
  cat("Error in GO GSEA:", conditionMessage(e), "\n")
  NULL
})

# === Display plots side by side if both exist ===
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)
} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)
} else {
  cat("No enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

No enriched terms in GSEA under pvalueCutoff.

# === KEGG Over-Representation Analysis (ORA) ===
result <- tryCatch({

  kegg_enrich <- enrichKEGG(
    gene           = gene_ids$ENTREZID,
    organism       = "hsa",
    pAdjustMethod  = "BH",
    pvalueCutoff   = 0.05
  )

  if (!is.null(kegg_enrich) && nrow(kegg_enrich@result) > 0) {

    # Spla13e results
    write.table(kegg_enrich@result,
                file = paste0(fin_name, "_KEGG_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e PNG plot
    png(paste0(fin_name, "_KEGG_OverRepresentation_Plot.png"), width = 1000, height = 800)
    print(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))
    dev.off()

    # Return plot object
    return(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))

  } else {
    cat("⚠️ No enriched KEGG terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in KEGG ORA:", conditionMessage(e), "\n")
  return(NULL)
})



# === KEGG Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  kegg_gse <- gseKEGG(
    geneList      = gene_list2,
    organism      = "hsa",
    minGSSize     = 120,
    pvalueCutoff  = 0.05,
    verbose       = FALSE
  )

  if (!is.null(kegg_gse) && nrow(kegg_gse@result) > 0) {

    # Spla13e results
    write.table(kegg_gse@result,
                file = paste0(fin_name, "_KEGG_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e PNG plot
    png(paste0(fin_name, "_KEGG_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))
    dev.off()

    # Return plot object
    return(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))

  } else {
    cat("⚠️ No enriched KEGG terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in KEGG GSEA:", conditionMessage(e), "\n")
  return(NULL)
})



# === Display Plots Nicely ===
# Set default figure size
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  # Side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No KEGG enrichment plots to display.\n")
}

Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...

Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

⚠️ No enriched KEGG terms found in GSEA.

NULL

library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(pathview)         # Optional for WikiPathways
library(dplyr)
library(DOSE)
library(rWikiPathways)

# === WikiPathways Over-Representation Analysis (ORA) ===
result <- tryCatch({

  wikipathways_enrich <- enrichWP(
    gene           = gene_ids$ENTREZID,
    organism       = "Homo sapiens",
    pvalueCutoff   = 0.05
  )

  if (!is.null(wikipathways_enrich) && nrow(wikipathways_enrich@result) > 0) {

    # Spla13e results
    write.table(wikipathways_enrich@result,
                file = paste0(fin_name, "_WikiPathways_ORA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e plot
    png(paste0(fin_name, "_WikiPathways_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))
    dev.off()

    # Return plot object
    return(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))

  } else {
    cat("⚠️ No enriched WikiPathways terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in WikiPathways ORA:", conditionMessage(e), "\n")
  return(NULL)
})



# === WikiPathways Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  wikipathways_gse <- gseWP(
    gene          = gene_list2,
    organism      = "Homo sapiens",
    pvalueCutoff  = 0.05
  )

  if (!is.null(wikipathways_gse) && nrow(wikipathways_gse@result) > 0) {

    # Spla13e results
    write.table(wikipathways_gse@result,
                file = paste0(fin_name, "_WikiPathways_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e plot
    png(paste0(fin_name, "_WikiPathways_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))
    dev.off()

    # Return plot object
    return(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))

  } else {
    cat("⚠️ No enriched WikiPathways terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in WikiPathways GSEA:", conditionMessage(e), "\n")
  return(NULL)
})



# === Display plots ===
options(repr.plot.width = 5, repr.plot.height = 5)  # Default size

if (!is.null(result) && !is.null(result2)) {
  # Show both plots side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No WikiPathways enrichment plots to display.\n")
}

##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.

The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################

DOSE v4.0.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an
R/Bioconductor package for Disease Ontology Semantic and Enrichment
analysis. Bioinformatics. 2015, 31(4):608-609


Attaching package: 'rWikiPathways'


The following object is masked from 'package:edgeR':

    getCounts


The following object is masked from 'package:GenomeInfoDb':

    listOrganisms


using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

⚠️ No enriched WikiPathways terms found in GSEA.

NULL

library(ReactomePA)
library(clusterProfiler)
library(enrichplot)
library(cowplot)

# === Reactome Over-Representation Analysis (ORA) ===
result <- tryCatch({

  reactome_ora <- enrichPathway(
    gene           = gene_ids$ENTREZID,
    organism       = "human",
    pAdjustMethod  = "BH",
    pvalueCutoff   = 0.05
  )

  if (!is.null(reactome_ora) && nrow(reactome_ora@result) > 0) {

    # Spla13e results
    write.table(reactome_ora@result,
                file = paste0(fin_name, "_Reactome_ORA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e plot
    png(paste0(fin_name, "_Reactome_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))
    dev.off()

    # Return plot for screen
    return(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))

  } else {
    cat("⚠️ No enriched Reactome terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in Reactome ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Reactome GSEA Analysis ===
result2 <- tryCatch({

  reactome_gsea <- gsePathway(
    gene            = gene_list2,
    organism        = "human",
    pAdjustMethod   = "BH",
    pvalueCutoff    = 0.05
  )

  if (!is.null(reactome_gsea) && nrow(reactome_gsea@result) > 0) {

    # Spla13e results
    write.table(reactome_gsea@result,
                file = paste0(fin_name, "_Reactome_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Spla13e plot
    png(paste0(fin_name, "_Reactome_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))
    dev.off()

    # Return plot for screen
    return(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))

  } else {
    cat("⚠️ No enriched Reactome terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in Reactome GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  # Show both side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No ReDC13tome enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

⚠️ No enriched Reactome terms found in GSEA.

NULL

library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)

# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C2")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)

# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID  # for ORA

# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({

  msig_enrich <- enricher(
    gene       = gene_list,
    TERM2GENE  = C2_t2g
  )

  if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {

    write.table(msig_enrich@result,
                file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
    dev.off()

    return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))

  } else {
    cat("⚠️ No significant MSigDB pathways found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  msig_gsea <- GSEA(
    geneList   = gene_list2,
    TERM2GENE  = C2_t2g,
    pvalueCutoff = 0.05
  )

  if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {

    write.table(msig_gsea@result,
                file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
    dev.off()

    return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))

  } else {
    cat("⚠️ No significant MSigDB GSEA pathways found.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 20, repr.plot.height = 20)

if (!is.null(result) && !is.null(result2)) {
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No MSigDB enrichment plots to display.\n")
}

Warning message:
"The `category` argument of `msigdbr()` is deprecated as of msigdbr 9.0.0.
ℹ Please use the `collection` argument instead."
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

⚠️ No significant MSigDB GSEA pathways found.

NULL

library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)

# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C5")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)

# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID  # for ORA

# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({

  msig_enrich <- enricher(
    gene       = gene_list,
    TERM2GENE  = C2_t2g
  )

  if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {

    write.table(msig_enrich@result,
                file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
    dev.off()

    return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))

  } else {
    cat("⚠️ No significant MSigDB pathways found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  msig_gsea <- GSEA(
    geneList   = gene_list2,
    TERM2GENE  = C2_t2g,
    pvalueCutoff = 0.05
  )

  if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {

    write.table(msig_gsea@result,
                file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
    dev.off()

    return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))

  } else {
    cat("⚠️ No significant MSigDB GSEA pathways found.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 10, repr.plot.height = 10)

if (!is.null(result) && !is.null(result2)) {
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No MSigDB enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

Warning message in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
"There are ties in the preranked stats (1.16% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results."
no term enriched under specific pvalueCutoff...

⚠️ No significant MSigDB GSEA pathways found.

NULL

	Geneid	Chr	Start	End	Strand	Length	CS13_1.CS13_1.CLAM.genomicAllAligned.sorted.bam	CS13_1in.CS13_1in.CLAM.genomicAllAligned.sorted.bam	CS13_2.CS13_2.CLAM.genomicAllAligned.sorted.bam	CS13_2in.CS13_2in.CLAM.genomicAllAligned.sorted.bam	DC13_1.DC13_1.CLAM.genomicAllAligned.sorted.bam	DC13_1in.DC13_1in.CLAM.genomicAllAligned.sorted.bam	DC13_2.DC13_2.CLAM.genomicAllAligned.sorted.bam	DC13_2in.DC13_2in.CLAM.genomicAllAligned.sorted.bam	pla13_1.pla13_1.CLAM.genomicAllAligned.sorted.bam	pla13_1in.pla13_1in.CLAM.genomicAllAligned.sorted.bam	pla13_2.pla13_2.CLAM.genomicAllAligned.sorted.bam	pla13_2in.pla13_2in.CLAM.genomicAllAligned.sorted.bam
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1	14421;21321;21621	14521;21421;21721	+;+;+	303	15	19	10	11	18	27	27	33	21	25	78	45
2	ENSG00000310526	chr1;chr1;chr1	14656;16856;21956	14756;16956;22056	-;-;-	303	134	99	111	86	193	105	137	133	165	144	331	252

	Geneid	Chr	Start	End	Strand	Length	CS13_1.CS13_1	CS13_1in.CS13_1in	CS13_2.CS13_2	CS13_2in.CS13_2in	DC13_1.DC13_1	DC13_1in.DC13_1in	DC13_2.DC13_2	DC13_2in.DC13_2in	pla13_1.pla13_1	pla13_1in.pla13_1in	pla13_2.pla13_2	pla13_2in.pla13_2in
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1	14421;21321;21621	14521;21421;21721	+;+;+	303	15	19	10	11	18	27	27	33	21	25	78	45
2	ENSG00000310526	chr1;chr1;chr1	14656;16856;21956	14756;16956;22056	-;-;-	303	134	99	111	86	193	105	137	133	165	144	331	252

	Geneid	Chr	Start	End	Strand	Length	CS13_1	CS13_1in	CS13_2	CS13_2in	DC13_1	DC13_1in	DC13_2	DC13_2in	pla13_1	pla13_1in	pla13_2	pla13_2in
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1	14421;21321;21621	14521;21421;21721	+;+;+	303	15	19	10	11	18	27	27	33	21	25	78	45
2	ENSG00000310526	chr1;chr1;chr1	14656;16856;21956	14756;16956;22056	-;-;-	303	134	99	111	86	193	105	137	133	165	144	331	252

	Geneid	Chr	Start	End	Strand	Length	CS13_1	CS13_1in	CS13_2	CS13_2in	DC13_1	DC13_1in	DC13_2	DC13_2in	pla13_1	pla13_1in	pla13_2	pla13_2in
	<chr>	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1	14421	21721	+;+;+	303	15	19	10	11	18	27	27	33	21	25	78	45
2	ENSG00000310526	chr1	14656	22056	-;-;-	303	134	99	111	86	193	105	137	133	165	144	331	252

	Geneid	Chr	Start	End	Strand	Length	CS13_1	CS13_1in	CS13_2	CS13_2in	DC13_1	DC13_1in	DC13_2	DC13_2in	pla13_1	pla13_1in	pla13_2	pla13_2in
	<chr>	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1	14421	21721	+;+;+	303	15	19	10	11	18	27	27	33	21	25	78	45
2	ENSG00000310526	chr1	14656	22056	-;-;-	303	134	99	111	86	193	105	137	133	165	144	331	252
3	ENSG00000241860	chr1	139395	139495	-	101	16	6	10	13	12	5	28	15	13	9	102	18
4	ENSG00000310528	chr1	187386	187686	-;-;-	301	1	5	0	0	1	4	1	2	0	2	7	2
5	ENSG00000310527	chr1	187446	187546	-	101	0	0	0	0	0	0	0	0	0	0	0	0
6	ENSG00000279457	chr1	187417	190017	-;-	202	6	9	5	3	13	13	14	18	16	3	20	17
7	ENSG00000293331	chr1	629954	633054	-;-;-;-;-;-;-;-	806	18	18	34	19	23	13	41	16	54	34	95	51
8	ENSG00000225880	chr1	629945	822845	-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-	2517	142	49	126	76	89	44	210	61	151	89	451	150

	CS13_1	CS13_2	DC13_1	DC13_2	pla13_1	pla13_2
ENSG00000290825	17.06505	11.69965	19.36198	25.16758	24.98516	46.29386
ENSG00000310526	152.44778	129.86606	207.60343	127.70214	196.31200	196.45213